"""simple docstring"""





from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch




@require_torch
@require_tf
class     lowerCAmelCase	(	a_    ):

     '''simple docstring'''







     def     lowerCamelCase__     (			self  :Optional[Any]     )			->      List[str]:




           """simple docstring"""



           UpperCamelCase__										=			SMALL_MODEL_IDENTIFIER
           UpperCamelCase__										=			"""pt"""
           UpperCamelCase__										=			"""tf"""







     def     lowerCamelCase__     (			self  :List[Any]				,      lowerCamelCase_  :List[str]     )			->      str:




           """simple docstring"""



           UpperCamelCase__										=			AutoModel.from_pretrained(self.test_model     )
           model_pt.save_pretrained(_lowercase     )







     def     lowerCamelCase__     (			self  :List[Any]				,      lowerCamelCase_  :Union[str, Any]     )			->      List[str]:




           """simple docstring"""



           UpperCamelCase__										=			TFAutoModel.from_pretrained(self.test_model				,      from_pt=_lowercase     )
           model_tf.save_pretrained(_lowercase     )







     def     lowerCamelCase__     (			self  :Union[str, Any]     )			->      Optional[Any]:




           """simple docstring"""



           UpperCamelCase__										=			"""mock_framework"""

           # Framework provided - return whatever the user provides
           UpperCamelCase__										=			FeaturesManager.determine_framework(self.test_model				,      _lowercase     )
           self.assertEqual(_lowercase				,      _lowercase     )

           # Local checkpoint and framework provided - return provided framework
           # PyTorch checkpoint
           with TemporaryDirectory() as local_pt_ckpt:
                 self._setup_pt_ckpt(_lowercase     )
                 UpperCamelCase__										=			FeaturesManager.determine_framework(_lowercase				,      _lowercase     )
                 self.assertEqual(_lowercase				,      _lowercase     )

           # TensorFlow checkpoint
           with TemporaryDirectory() as local_tf_ckpt:
                 self._setup_tf_ckpt(_lowercase     )
                 UpperCamelCase__										=			FeaturesManager.determine_framework(_lowercase				,      _lowercase     )
                 self.assertEqual(_lowercase				,      _lowercase     )







     def     lowerCamelCase__     (			self  :Tuple     )			->      Union[str, Any]:




           """simple docstring"""



           with TemporaryDirectory() as local_pt_ckpt:
                 self._setup_pt_ckpt(_lowercase     )
                 UpperCamelCase__										=			FeaturesManager.determine_framework(_lowercase     )
                 self.assertEqual(_lowercase				,      self.framework_pt     )

           # TensorFlow checkpoint
           with TemporaryDirectory() as local_tf_ckpt:
                 self._setup_tf_ckpt(_lowercase     )
                 UpperCamelCase__										=			FeaturesManager.determine_framework(_lowercase     )
                 self.assertEqual(_lowercase				,      self.framework_tf     )

           # Invalid local checkpoint
           with TemporaryDirectory() as local_invalid_ckpt:
                 with self.assertRaises(_lowercase     ):
                       UpperCamelCase__										=			FeaturesManager.determine_framework(_lowercase     )







     def     lowerCamelCase__     (			self  :Optional[Any]     )			->      List[Any]:




           """simple docstring"""



           UpperCamelCase__										=			MagicMock(return_value=_lowercase     )
           with patch("transformers.onnx.features.is_tf_available"				,      _lowercase     ):
                 UpperCamelCase__										=			FeaturesManager.determine_framework(self.test_model     )
                 self.assertEqual(_lowercase				,      self.framework_pt     )

           # PyTorch not in environment -> use TensorFlow
           UpperCamelCase__										=			MagicMock(return_value=_lowercase     )
           with patch("transformers.onnx.features.is_torch_available"				,      _lowercase     ):
                 UpperCamelCase__										=			FeaturesManager.determine_framework(self.test_model     )
                 self.assertEqual(_lowercase				,      self.framework_tf     )

           # Both in environment -> use PyTorch
           UpperCamelCase__										=			MagicMock(return_value=_lowercase     )
           UpperCamelCase__										=			MagicMock(return_value=_lowercase     )
           with patch("transformers.onnx.features.is_tf_available"				,      _lowercase     ), patch(
               "transformers.onnx.features.is_torch_available"				,      _lowercase     ):
                 UpperCamelCase__										=			FeaturesManager.determine_framework(self.test_model     )
                 self.assertEqual(_lowercase				,      self.framework_pt     )

           # Both not in environment -> raise error
           UpperCamelCase__										=			MagicMock(return_value=_lowercase     )
           UpperCamelCase__										=			MagicMock(return_value=_lowercase     )
           with patch("transformers.onnx.features.is_tf_available"				,      _lowercase     ), patch(
               "transformers.onnx.features.is_torch_available"				,      _lowercase     ):
                 with self.assertRaises(_lowercase     ):
                       UpperCamelCase__										=			FeaturesManager.determine_framework(self.test_model     )


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				:      Union[str, Any]   		=			logging.getLogger(__name__)



@dataclass
class        A__(a_							):
						"""simple docstring"""






						_A    :				Optional[float]       					=			field(
						    default=0.0,  metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''Whether to SortishSamler or not.'''}							)
						_A    :				bool       					=			field(
						    default=a_,  metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''whether to use adafactor'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(default=a_,  metadata={'''help''': '''Dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[str]       					=			field(
						    default='''linear''',  metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()							)}'''},  )


